Office: 611 KTH
Office Phone: x23604
email: jfox AT mcmaster.ca
Office Hours: Mondays 11:00-12:00, Wednesdays, 1:00-2:00, and by appointment
Abbreviated URL: tinyurl.com/soc761
Contents:
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Sociology 761 is an intermediate-level course in social statistics. The goals of the course are to expose students to statistical methods commonly employed in social research, to develop facility in using the computer for data analysis, and to provide background for reading the literature in social statistics. The class will meet on Wednesdays, 2:30--5:30 PM, in room KTH-712.
I assume that students have previously had a course in applied regression analysis roughly comparable to Sociology 740.
The course will introduce the following topics, devoting approximately three weeks to each:
(1) Mathematical background: basic matrix algebra; basic calculus; matrix calculus; linear least-squares regression in matrix form.
J. Fox, A Mathematical Primer for Social Statistics (Sage, 2009). Available at the bookstore. Alternative: on-line appendices to J. Fox, Applied Regression Analysis and Generalized Linear Models, Second Edition (Sage, 2008).
J. Fox, "Statistical Theory for Linear Models", Chapter 9 (Sec. 9.1-9.4), Applied Regression Analysis and Generalized Linear Models, Second Edition (Sage, 2008). (If you don't have a copy of this book from Soc. 740, I'll make a copy of the chapter available to you.)
J. Fox and S. Weisberg, "Working With Matrices", Section 8.2 in An R Companion to Applied Regression, Second Edition (Sage, 2011). (If you do not have a copy of this text and do not want to buy a copy, I'll make a copy of this section available to you.) (Also see under Computing.)
(2) Linear structural-equation models, including models with latent variables.
J. Fox, "Linear Structural-Equation Models", Chapter 4, Linear Statistical Models and Related Methods (Wiley, 1984).
J. Fox, "Structural Equation Modeling in R with the sem Package", on-line appendix, J. Fox and S. Weisberg, An R Companion to Applied Regression (Sage, 2011).
K. A. Bollen, "Latent Variables in Psychology and the Social Sciences", Annual Review of Psychology, 2002, 53: 605-634.
(3) Survival analysis ("event-history" analysis) for duration data.
P. Allison, Event History and Survival Analysis, Second Edition (Sage, 2014). Available at the bookstore.
J. Fox and S. Weisberg, "Cox Proportional-Hazards Regression for Survival Data in R", on-line appendix, J. Fox and S. Weisberg, An R Companion to Applied Regression (Sage, 2011).
(4) Linear (and, time permitting) generalized-linear mixed models for hierarchical and longitudinal data.
J. Fox, "Linear Mixed-Effects Models" and "Generalized Linear and Nonlinear Mixed-Effects Models", Ch. 23 and 24 (Sec. 24.1 only), from J. Fox, Applied Regression Analysis and Generalized Linear Models, Third Edition (Sage, forthcoming); preprint, to be distributed.
J. Fox and S. Weisberg, "Mixed-Effects Models in R" (draft), on-line appendix, An R Companion to Applied Regression, Second Edition (Sage, 2011).
A caveat: Each of the three statistical topics could easily be the subject of a one-semester course. My object is to introduce these topics in a manner that will provide a basis for applications and further reading.
Computing in the course will be done in R, a free implementation of the S statistical computing language and environment. R has become the standard language for statistical computing among statisticians and is now in very wide use, including in the social sciences. We will use R through the free RStudio interactive development environment (IDE): See the installation instructions for information about how to install R and RStudio on Windows, Mac OS X, and Linux systems.
If necessary, I will schedule an optional introductory lecture on R for students who are unfamiliar with it. If you are unfamiliar with R, you will probably want to get a copy of Fox and Weisberg, An R Companion to Applied Regression, Second Edition (Sage, 2011), available at the bookstore.
Each topic will be accompanied by a homework problem set distributed at the start of the topic. Homework will be collected (one week after the topic ends) and corrected, but not graded. You will receive credit for homework assignments showing reasonable effort that are submitted on time. Students are encouraged to collaborate on the homework if this facilitates their progress in the course. After the initial topic, homework assignments will focus on data analysis. You are also encouraged to use R Markdown in RStudio, using the template provided, to write your data-analysis homework assignments; if you do so for all of the data-analysis homework, you will receive an extra 2 percent on your final course grade.
Each student in the class will write and present a short paper (approximately 15 pages, including tables, figures, and references; presentation about 25 minutes) demonstrating the application of a statistical method to data. The focus may either be on a substantive application of the method or on explaining the method, using an example. Papers will be presented at a "mini-conference" on December 3.
The selected method should be different from those covered in this course and in Sociology 740. Some examples of appropriate topics include multiple imputation of missing data; nonlinear regression analysis; nonparametric regression analysis; robust regression; regression with time-series data; exploratory factor analysis; bootstrapping; or extensions of the methods studied in the class (e.g., structural-equation models for categorical endogenous variables, nonlinear mixed models). I will be responsible for providing introductory readings on selected topics.
Time-table for the paper: September 26, topic selection; November 24, draft of paper due; December 12, final paper due.
Homework, 40 percent; draft paper, 20 percent; final paper, 35 percent; presentation, 5 percent.
The standard grading scheme will be used to translate percentage grades on the exams into letter grades. Please note that it is possible to get a grade of A+ in this class, but I give this grade based upon my judgment rather than by mechanical translation of percentages into letter grades; therefore, a percentage grade of 90 will not necessarily (and in fact would generally not) produce a letter grade of A+.
If you need help please do not hesitate to contact me. My office hours for the course are Wednesdays, 1:00-2:00 PM. You may contact me by email at jfox AT mcmaster.ca or by phone at 905-525-9140x23604. You can also post a question to the course email list, at d-soc761 AT mcmaster.ca.
As the semester progresses, I will make my lecture slides available in the form of PDF (portable-document format) files and the R "scripts" for the lectures in plain-text (ascii) files.
Date | Lecture Notes | R Scripts and Data Files | Homework Exercises |
Sept. 10, 17 | Introduction to Matrices | matrices.R, Thurstone.txt | matrices (answers) |
Tues., Sept. 22 | Introduction to R (optional session, if necessary) | R-introduction.R, Rmd template, example Rmd document, Duncan.txt | |
Sept. 24 | Very Quick Calculus | calculus.R | calculus (answers) |
Oct. 1 | Linear Models Using Matrices | linear-models.R | linear models (answers) |
Oct. 8, 15, 22 | Structural-Equation Models | SEMs.R, Rindfuss.R, Lincoln.R, Wheaton.R | SEMs (answers) |
Oct. 29, Nov. 5, 12 |
Survival Analysis | survival-analysis.R, lifeTable.R, unfold.R, Canada-mortality.txt, Rossi.txt, Henning.R, Henning.txt | survival analysis (answers) |
Nov. 19, 26 | Mixed-Effect Models | mixed-models.R, Snijders.R, Snijders.txt | mixed models (answers) |
Dec. 3 | Presentations |
If you do not have a PDF-file viewer, you can download the Adobe Reader viewer free from the Adobe Web site.
Academic dishonesty consists of misrepresentation by deception or by other fraudulent means and can result in serious consequences, e.g. the grade of zero on an assignment, loss of credit with a notation on the transcript (notation reads: "Grade of F assigned for academic dishonesty"), and/or suspension or expulsion from the university.
It is your responsibility to understand what constitutes academic dishonesty. For information on the various kinds of academic dishonesty please refer to the Academic Integrity Policy, specifically Appendix 3, located at <http://www.mcmaster.ca/policy/Students-AcademicStudies/AcademicIntegrity.pdf>.
The following illustrates only three forms of academic dishonesty:
Do NOT fax assignments. Please see your instructor for the most appropriate way to submit assignments.
The Sociology staff do NOT date-stamp assignments, nor do they monitor the submission or return of papers.Computer use in the classroom is intended to facilitate learning in that particular lecture or tutorial. At the discretion of the instructor, students using a computer for any other purpose may be required to turn the computer off for the remainder of the lecture or tutorial.
The instructor and university reserve the right to modify elements of the course during the term. The university may change the dates and deadlines for any or all courses in extreme circumstances. If either type of modification becomes necessary, reasonable notice and communication with the students will be given with explanation and the opportunity to comment on changes. It is the responsibility of the student to check his/her McMaster email and course websites weekly during the term and to note any changes.
It is the policy of the Faculty of Social Sciences that all e-mail communication sent from students to instructors (including TAs), and from students to staff, must originate from the student’s own McMaster University e-mail account. This policy protects confidentiality and confirms the identity of the student. It is the student’s responsibility to ensure that communication is sent to the university from a McMaster account. If an instructor becomes aware that a communication has come from an alternate address, the instructor may not reply at his or her discretion.